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Gerd Heber, The HDF Group and Haymo Kutschbach,* ILNumerics

Metaphorically speaking, this blog post is about a frog trying to climb out of a well, a damp and unsightly corner of the HDF5 ecosystem called HDF5.NET. People who know more about its genesis tell us that it was never intended as what it became to be perceived as, an “aspirational” .NET interface for HDF5 that would one day be complete and fully supported. Be that as it may, it’s important to ask, “What can we do today to better serve the needs of the .NET community?” We believe, as the title suggests, we need to take a step back to move forward.

The ESIP Federation comes together twice each year to discuss topics around changing technology, data, information and knowledge in support of society. ESIP meetings are interdisciplinary and inclusive. Among the attendees are Earth science data and information technology practitioners; researchers representing a variety of scientific domains that include land, atmosphere, ocean, solid earth, ecology, data and social sciences; science educators; and anyone working in science and technology-related fields who is interested in advancing Earth science information best practices in an open and transparent fashion.

UPDATE January 19, 2016: The HDF5-1.10.0-alpha1 release is now available, adding Collective Metadata I/O to these features:

We’re pleased to announce the release of HDF5 1.10.0-alpha0.

HDF5 1.10.0, planned for release in Spring, 2016, is a major release containing many new features. On January 6, 2016 we announced the release of the first alpha version of the software.

The alpha0 release contains some (but not all) of the features that will be in HDF5 1.10.0. The Single Writer/Multiple Reader and Virtual Data Set features, below, are both contained in this alpha release as are scalable chunk indexing and persistent free file space tracking. More features, such as enhancements to parallel HDF5 and support for compressing contiguous datasets will be added in upcoming alpha releases.

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John Readey, The HDF Group

Editor’s Note: Since this post was written in 2015, The HDF Group has developed Kita, a new product that addresses the challenges of adapting large scale array-based computing to the cloud and object storage while intelligently handling the full data management life cycle. If this is something that interests you, we’d love to hear from you.

HDF Server is a new product from The HDF Group which enables HDF5 resources to be accessed and modified using Hypertext Transfer Protocol (HTTP).

HDF Server [1], released in February 2015, was first developed as a proof of concept that enabled remote access to HDF5 content using a RESTful API. HDF Server version 0.1.0 wasn’t yet intended for use in a production environment since it didn’t initially provide a set of security features and controls. Following its successful debut, The HDF Group incorporated additional planned features. The newest version of HDF Server provides exciting capabilities for accessing HDF5 data in an easy and secure way.

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We are currently planning for a Q2 2016 release of the product. In the meantime, we are working with a few early adopters on finalizing the initial feature set. If you have additional questions about HDF5/ODBC, or if you would like to become an early adopter, please contact us ...

John Readey, The HDF Group

We’re pleased to announce that The HDF Group is now a member of the Open Commons Consortium (formerly Open Cloud Consortium), a not for profit that manages and operates cloud computing and data commons infrastructure to support scientific, medical, health care and environmental research.

The HDF Group will be participating in the NOAA Data Alliance Working Group (WG) on the WG committee that will determine the datasets to be hosted in the NOAA data commons as well as tools to be used in the computational ecosystem surrounding the NOAA data commons.

OSDC website

“The Open Commons Consortium (OCC) is a truly innovative concept for supporting scientific computing,” said Mike Folk, The HDF Group’s President. “Their cloud computing and data commons infrastructure supports a wide range of research, and OCC’s membership spans government, academia, and the private sector. This is a good opportunity for us to learn about how we can best serve these communities.”

The HDF Group will also participate in the Open Science Data Cloud working group and receive resource allocations on the OSDC Griffin resource. The HDF Group’s John Readey is working with the OCC and others to investigate ways to use Griffin effectively. Readey says, “Griffin is a great testbed for cloud-based systems. With access to object storage (using the AWS/S3 api) and the ability to programmatically create VM’s, we will explore new methods for the analysis of scientific datasets.”

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Joel Plutchak, The HDF Group
The HDF Group’s support for and use of the Java Programming Language consists of Java wrappers for the HDF4 and HDF5 C libraries, an Object Model definition and implementation, and HDFView, a graphical file viewing application. In this article we'll discuss what we’re doing now with Java, and look toward the future.
[caption id="attachment_10769" align="alignright" width="300"] The screen capture shows some of the capabilities of the HDFView application. Displayed is a JPSS Mission VIIRS (Visible Infrared Imaging Radiometer Suite) Day-Night band dataset in table form and image form with false color palette attached.[/caption]
By the time the first public version of the Java Programming Language was released in 1995, various groups at the University of Illinois were already...

Anthony Scopatz, Assistant Professor at the University of South Carolina, HDF guest blogger
"Python is great and its ecosystem for scientific computing is world class. HDF5 is amazing and is rightly the gold standard for persistence for scientific data. Many people use HDF5 from Python, and this number is only growing due to pandas’ HDFStore. However, using HDF5 from Python has at least one more knot than it needs to. Let’s change that."
Almost immediately when going to use HDF5 from Python you are faced with a choice between two fantastic packages with overlapping capabilities: h5py and PyTables. h5py wraps the HDF5 API more closely using autogenerated Cython. PyTables, while also wrapping HDF5, focuses more on a Table data structure and adds...